Data driven CS reconstruction Duarte et al. [1]} propose an approach for simultaneous learning of the sensing matrix and dictionary atoms. Due to the small patch size of the atoms, their usage for compressive image reconstruction is limited to local multiplexing, unlike the actual SPC involving global multiplexing of the scene. Reconstruction algorithms using convolutional neural networks (CNNs) typical take input as measurements from an image patch and try to output the image back by minimizing the reconstruction loss. Kulkarni et al. [2]} proposed ReconNet, Yao et al. [3]} proposed \(DR^2\) -Net having residual connections for reconstruction. Although these approaches lead to a non-iterative and hence faster inference, being task specific, they only work for the fixed settings of the sensing matrix and measurement rates used for training. Changing the settings requires retraining the architecture which is not very appealing. Also, being patch-wise, they also fail to account for global multiplexing in SPC.
